# 🤖 Cross-Domain Transfer Learning: Human Motion to Robot Fault Detection

Welcome to the **Cross-Domain Transfer Learning** project, where we bridge the gap between human motion analysis and robotic fault detection. This repository provides a comprehensive approach to using advanced machine learning techniques, specifically LSTM-based residual models, to enhance robotic systems' reliability and performance. 

## 📚 Overview

The goal of this project is to leverage human motion data to train a model that can detect faults in robotic joints. By preprocessing data, mapping human motion features to robotic contexts, and applying transfer learning, we achieve improved fault detection. The method incorporates:

- Data preprocessing techniques to clean and prepare human motion data.
- Feature adaptation that aligns robot features with human-like patterns.
- Fine-tuning of LSTM models, which helps the model learn efficiently while maintaining earlier learned knowledge.

### 🔑 Key Features

- **LSTM-based Model:** The core of our approach is a Long Short-Term Memory (LSTM) model, chosen for its ability to capture temporal dependencies in sequential data.
- **Residual Learning:** By employing residual connections, the model can learn better representations, improving fault detection accuracy.
- **Transfer Learning:** We utilize a transfer learning approach to apply knowledge gained from human motion data to robotic applications.
- **Class Weighting and Callbacks:** The evaluation process incorporates class weighting and callbacks for improved model performance.

## 📊 Getting Started

To get started with the project, follow the steps below:

### 1. Clone the Repository

Use the following command to clone the repository:

```bash
git clone https://github.com/FabianCormier/Cross-Domain-transfer-learning-from-Human-Motion-to-Robot-Fault-Detection.git
cd Cross-Domain-transfer-learning-from-Human-Motion-to-Robot-Fault-Detection

2. Install Requirements

Install the necessary packages. Use pip to install the required dependencies:

pip install -r requirements.txt

3. Data Preparation

Prepare your human motion data. Ensure that your dataset is organized correctly. Follow the guidelines provided in the data/README.md file for detailed instructions.

4. Training the Model

Run the training script to start training the LSTM model:

python train.py

Monitor the training process. The script includes logs that display real-time performance metrics.

5. Evaluating the Model

Once training is complete, evaluate the model using the following command:

python evaluate.py

The evaluation process will yield performance metrics and a feature importance analysis report.

🔧 Technology Stack

• Python 3: The primary programming language used in this project.
• TensorFlow: The machine learning library employed for building and training the models.
• NumPy & Pandas: Essential libraries for data manipulation and analysis.
• Matplotlib & Seaborn: Libraries used for visualizing data and model performance.

🔗 Links

• Check the Releases section for downloadable files and model checkpoints.

🛠 Topics

This repository covers a wide range of topics related to machine learning and fault detection, including:

• bilstm-model
• feature
• feature-adaptation
• feature-engineering
• fine-tuning
• lstm-model
• lstm-neural-networks
• nn
• python3
• rnn
• tensorflow

🎓 Documentation

For in-depth explanations and methodologies, refer to the following sections of the repository:

• data/ - Documentation on dataset structure and preprocessing methods.
• models/ - Details about the LSTM architecture and model configurations.
• notebooks/ - Jupyter notebooks showcasing various experiments and visualizations.

👥 Contributing

We welcome contributions! If you would like to enhance the project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your branch and create a pull request.

Please ensure your code adheres to our coding standards and includes appropriate documentation.

📝 License

This project is licensed under the MIT License. See the LICENSE file for more details.

📞 Contact

For questions, suggestions, or feedback, please reach out to:

• Fabian Cormier - GitHub Profile
• Email - [email protected]

🎉 Acknowledgments

We would like to thank the following resources and libraries for making this project possible:

• TensorFlow for providing an excellent framework for building neural networks.
• The community for contributions and support.

📈 Future Work

As technology evolves, so does the need for improved methodologies in fault detection. Future iterations of this project may explore:

• Integration of additional data sources for enhanced model training.
• Application of real-time monitoring techniques.
• Collaboration with other machine learning frameworks.

We hope this project serves as a useful resource in the field of robotics and machine learning. Your feedback and contributions will help us improve the system further. Happy coding! 😊